Search Results for author:
Found 11 papers, 5 papers with code
Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models.
A Survey on Large Language Model-Based Game Agents
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI).
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
In most FL approaches, all edge clients are assumed to have sufficient computation capabilities to participate in the learning of a deep neural network (DNN) model.
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
We investigate nonlinear regression for nonstationary sequential data.
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values.
Achieving Online Regression Performance of LSTMs with Simple RNNs
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to generalize nonlinear temporal dependencies.
Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels
We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process.
RNN-based Online Learning: An Efficient First-Order Optimization Algorithm with a Convergence Guarantee
We investigate online nonlinear regression with continually running recurrent neural network networks (RNNs), i. e., RNN-based online learning.
Stability of the Decoupled Extended Kalman Filter Learning Algorithm in LSTM-Based Online Learning
We investigate the convergence and stability properties of the decoupled extended Kalman filter learning algorithm (DEKF) within the long-short term memory network (LSTM) based online learning framework.
